Remote+Sensing+Methods+Outline

= Remote Sensing Methods Outline =
 * Landsat-5 imagery provided by instructors (determine source from instructors)
 * Use ERDAS Imagine 2010 to perform image analysis
 * Unsupervised image processing utility was used to classify image into 12 classes
 * These 12 classes were categorized one-by-one
 * 1) Water
 * 2) Roads
 * 3) Forest / Woods
 * 4) Forest / Forest
 * 5) Forest / Open Land
 * 6) Townhouse / Apartment
 * 7) Single Family Residential
 * 8) Low Density Residential
 * 9) Commercial
 * 10) Commercial / Rooftops
 * 11) Office / Light Industrial
 * 12) Agricultural
 * Google Earth was used to assist in identifying the major feature being classified
 * Classified Landsat-5 image was loaded into ESRI's Arcmap 10.1 to obtain percentage of classes
 * The overall site image was clipped using the watershed boundaries for Marsh & Long Island Creek
 * 2 separate images were created; one for each watershed
 * The 12 classes were further reduced to 9 classes
 * 1) Water
 * 2) Forest
 * 3) Agriculture
 * 4) Roads
 * 5) Single Family Residential
 * 6) Low Density Residential
 * 7) Townhouse / Apartment
 * 8) Office / Light Industrial
 * 9) Commercial
 * Using the clipped, classified image, the total number of pixels were determined
 * The sum of each category's pixel count was determined
 * Percentages were calculated into the attribute table of the image by using the following formula:
 * PERCENTAGE = (([Classified Pixel Count] / [Total Pixel Count])*100) * [Landuse Coefficient]
 * This process was performed for each year of Landsat5 imagery.